Disability associated with mental illness is a major contributor to the global burden of disease. The present study looks at some aspects of disability associated with 7 psychiatric disorders: schizophrenia, bipolar affective disorder, anxiety disorders, depression, obsessive-compulsive disorder, dementia, and mental and behavioural disorders due to the use of alcohol.

Mental disorders are common in the general population; every year about 25% of the total European population is affected by a mental condition. The prevalence of psychiatric disorders might be underestimated. Emerging evidence highlights the role of immune response as a key factor in MDs. Immunological biomarkers seem to be related to illness progression and to treatment effectiveness; several studies suggest strong associations among IL-6, TNFa, S100b, IL 1b, and PCR with affective or schizophrenic disorders. The purpose of this review is to examine and to understand the possible link between mental disorders and interleukin 33 to clarify the role of this axis in the immune system. We found 13 research papers that evaluated interleukin 33 or interleukin 31 levels in subjects affected by mental disorders. Eight studies investigated cytokines in affective disorders. Three studies measured levels of IL-33 in schizophrenia and two studies focused on patients affected by autism spectrum disorders. Alterations in brain structure and neurodevelopmental outcome are affected by multiple levels of organization. Disorders of the autoimmune response, and of the IL-33/31 axis, may therefore be one of the factors involved in this process. These results support the evidence that alarmins, particularly the IL-33/31 axis, need more consideration among researchers and practitioners.

Despite abundant research into the neurobiology of mental disorders, to date neurobiological insights have had very little impact on psychiatric diagnosis or treatment. In this review, we contend that the search for neuroimaging biomarkers-neuromarkers-of mental disorders is a highly promising avenue toward improved psychiatric healthcare. However, many of the traditional tools used for psychiatric neuroimaging are inadequate for the identification of neuromarkers. Specifically, we highlight the need for larger samples and for multivariate analysis. Approaches such as machine learning are likely to be beneficial for interrogating high-dimensional neuroimaging data. We suggest that broad, population-based study designs will be important for developing neuromarkers of mental disorders, and will facilitate a move away from a phenomenological definition of mental disorder categories and toward psychiatric nosology based on biological evidence. We provide an outline of how the development of neuromarkers should occur, emphasizing the need for tests of external and construct validity, and for collaborative research efforts. Finally, we highlight some concerns regarding the development, and use of, neuromarkers in psychiatric healthcare.

Life satisfaction refers to a cognitive and global evaluation of the quality of one's life as a whole. The arguably most often used measure of life satisfaction is the Satisfaction With Life Scale (SWLS). Persons with mental disorders generally report lower SWLS scores than healthy controls, yet there is a lack of studies that have compared different diagnostic groups, tested measurement invariance of the SWLS across these groups, and examined effects of treatment on life satisfaction.

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

Mental disorders constitute a major public health problem globally with higher burden in low and middle-income countries. In Bangladesh, systematically-collected data on mental disorders are scarce and this leaves the extent of the problem not so well defined. We reviewed the literature on mental health disorders in Bangladesh to summarize the available data and identify evidence gaps.

To estimate treatment rates of 12-month mental disorders in the Saudi National Mental Health Survey (SNMHS).

This article provides a review of the magnitude of mental disorders in children and adolescents from recent community surveys across the world. Although there is substantial variation in the results depending upon the methodological characteristics of the studies, the findings converge in demonstrating that approximately one fourth of youth experience a mental disorder during the past year, and about one third across their lifetimes. Anxiety disorders are the most frequent conditions in children, followed by behavior disorders, mood disorders, and substance use disorders. Fewer than half of youth with current mental disorders receive mental health specialty treatment. However, those with the most severe disorders tend to receive mental health services. Current issues that are now being identified in the field of child psychiatric epidemiology include: refinement of classification and assessment, inclusion of young children in epidemiologic surveys, integration of child and adult psychiatric epidemiology, and evaluation of both mental and physical disorders in children.

To identify and synthesise the literature on the cost of mental disorders.

During the execution of sentences in prison units, there are deficiencies within the adaptation mechanisms of persons deprived of liberty, this varies depending on the place where they serve the sanction and the length of stay in it.

The COVID-19 pandemic has increased the impact and spread of mental illness and made health services difficult to access; therefore, there is a need for remote, pervasive forms of mental health monitoring. Digital phenotyping is a new approach that uses measures extracted from spontaneous interactions with smartphones (eg, screen touches or movements) or other digital devices as markers of mental status.

The employment of clinical databases in the study of mental disorders is essential to the diagnosis and treatment of patients with mental illness. While text corpora obtain merely limited information of content, speech corpora capture tones, emotions, rhythms and many other signals beyond content. Hence, the design and development of speech corpora for patients with mental disorders is increasingly important.

The present study aims to examine the association between religious involvement and mental disorder (anxiety disorder, mood disorder, alcohol use disorder) in a general Chinese population, and explore connections between religious belief and mental disorders in the Hui and Han ethnic groups.

World population growth is projected to be concentrated in megacities, with increases in social inequality and urbanization-associated stress. São Paulo Metropolitan Area (SPMA) provides a forewarning of the burden of mental disorders in urban settings in developing world. The aim of this study is to estimate prevalence, severity, and treatment of recently active DSM-IV mental disorders. We examined socio-demographic correlates, aspects of urban living such as internal migration, exposure to violence, and neighborhood-level social deprivation with 12-month mental disorders.

We review here recent original research and meta-analytic evidence on the associations of maternal hypertensive pregnancy disorders and mental and behavioral disorders in the offspring.

To estimate lifetime treatment rates of mental disorders in the Saudi National Mental Health Survey (SNMHS).

The WHO Global Burden of Disease study estimates that mental and addictive disorders are among the most burdensome in the world, and their burden will increase over the next decades. The mental and behavioral disorders account for about 12% of the global burden of disease. However, these estimates and projections are based largely on literature review rather than cross-national epidemiological surveys. In India, little is known about the extent, severity and unmet need of treatment mental disorders. Thus, there was a need to carry out rigorously implemented general population surveys that estimate the prevalence of mental disorders among urban population at Pune, Maharashtra. The study attempted to address unmet need and to form a basis for formulating the mental health need of the community.

Common mental disorders (CMDs) are major causes of sickness absence and disability. Prevention requires knowledge of how individuals perceive causal mechanisms, and in this study we sought to examine work-related factors as causal attribution of CMDs.

Animal models of mental illness provide a foundation for evaluating hypotheses for the mechanistic causes of mental illness. Neurophysiological investigations of neural network activity in rodent models of mental dysfunction are reviewed from the conceptual framework of the discoordination hypothesis, which asserts that failures of neural coordination cause cognitive deficits in the judicious processing and use of information. Abnormal dynamic coordination of excitatory and inhibitory neural discharge in pharmacologic and genetic rodent models supports the discoordination hypothesis. These observations suggest excitation-inhibition discoordination and aberrant neural circuit dynamics as causes of cognitive impairment, as well as therapeutic targets for cognition-promoting treatments.

Population-based studies provide the understanding of health-need required for effective public health policy and service-planning. Mental disorders are an important but, until recently, neglected agenda in global health. This paper reviews the coverage and limitations in global epidemiological data for mental disorders and suggests strategies to strengthen the data.